Download ppt - Chapter 12 Notes Surveys, Sampling, & Bias Examine a Part of the Whole: We’d like to know about an entire population of individuals, but examining all

Chapter 12 Notes Surveys, Sampling, & Bias

Examine a Part of the Whole:We’d like to know about an entire population of individuals, but examining all of them is usually impractical (examining an entire population is called a census) if not impossible. So we settle for examining a smaller group – a sample selected from the population.


Sample surveys are designed to ask questions of a small group of people in the hope of learning something about the entire population they represent.

The sampling frame is a list of individuals from which the sample is drawn.


A sample is said to be representative or unbiased if the statistics we compute for the sample reflect the corresponding parameters accurately.

Sampling methods that, by their nature, tend to over- or under- emphasize some characteristics of the population are said to be biased. There are many forms of bias that we will examine.


Types of Bias:

• Voluntary Response – We ask everyone is our population to respond voluntarily. Problems: difficulty defining our sampling frame, often these people have the strongest opinions.


Types of Bias:

• Convenience Sampling – We include the individuals/items that are most convenient. Problem: not representative of the population of interest.


Types of Bias:

• Undercoverage – Some portion of the population is either not sampled at all or has a smaller representation in the sample than it does in the population. Problem: not representative of the population of interest.


Types of Bias:

• Nonresponse – Some portion of the population of interest does not respond for whatever reason. Problem: not representative of the population of interest.


Types of Bias:

Response – Anything in the survey design that influences the responses. Problem: Responses are influenced/prejudiced.


Bad samples yield worthless data. Many of the most convenient forms of sampling can be seriously biased. There is no way to correct for the bias of a bad sample. Throw your data out and start over. Pay attention to sampling design and beware of reports based on poor samples.


You must Randomize. Randomizing helps protects us against factors we know are in the data and against factors that we may be unaware of. It protects against bias.

What sample size do you need? There is no hard & fast rule. You need a large enough sample to accurately represent the population


Remember that values that represent entire populations are called population parameters

Values that represent a sample are called sample statistics


If we randomly sample, we do not expect to get the same group of items every time. This leads us to the idea that we do not get the same numerical results every time. This is called sampling variability.


Sampling Methods:

• Simple Random Sample (SRS) : Each item must have an equal chance of being selected and each combination of items must have an equal chance of being selected as well.


Sampling Methods:• Stratified Random Sample: The

population is first divided into homogenous groups called strata, then we take a SRS from each stratum and combine our results. We sometimes employ this method when we believe the groups will behave differently. It helps reduce sampling variability.


Sampling Methods:• Cluster Sample: The population is split into

parts or clusters. Then we select a few clusters at random and perform a census or SRS within each one. We employ this method to make a sampling task more practical or affordable. The result should be unbiased as long as we believe each cluster is representative of the entire population.


Sampling Methods:• Systematic Sample: We draw a sample by selecting

individuals systematically from the population. To make it random, you must start the systematic sample from a randomly selected place in the list. You must arrange the system so that every item has the same probability of being part of the sample at the beginning. We can also employ this method to make a sampling task more practical or affordable. When there is no reason to believe that the order of the list can be associated in any way with the responses sought, then this method should produce an unbiased sample also.


Sampling Methods:

• Multi-stage Sample: Sampling schemes that combine several of these methods are called multi-stage samples. Multi-stage samples are often employed by professional polling organizations.